In Table REF , we compared the embedding method between 3D-GCN [1]} and Pointformer [2]} on TransNet. Modalities for generalized point cloud were depth, RGB and ray direction (without surface normal) for all the trials. The only differences between them were depth type and embedding methods. With ground truth input, 3D-GCN and Pointformer achieved similar results. For some metrics, i.e. \(5^{\circ }5cm\) , 3D-GCN was even better.
But when the ground truth depth was changed to estimated depth (modeling the change from opaque to transparent setting), Pointformer retained much more accuracy than 3D-GCN. Here is our explanation. Like many point cloud embedding methods, 3D-GCN propagates information between nearest neighbors. It is a very efficient method given a point cloud with low noise. But given the completed depth, high noise makes it unstable to pass data among neighbors. While for Pointformer, information is passed through the whole point cloud, no matter how large the noise is. Therefore, given depth information with large uncertainty, the transformer-based embedding method might be more powerful than embedding methods using nearest neighbors.
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